hiring is broken. we all know it, but let me show you the numbers.
the time problem
recruiters spend an average of 6-7 seconds scanning a resume before deciding to move forward or reject. then they schedule a 30-min screening call. then maybe a technical interview. that's 2-4 hours per candidate (if it's not stretched out for days) before realizing they're not a fit.
LinkedIn's Talent Blog confirms what we all know: "too many companies hire the candidate who delivers the best performance in their interviews" - not the one who's actually the best fit for the job.
the average cost-per-hire is now $4,700 according to industry benchmarks, but for specialized tech roles it can exceed $30,000 when you factor in lost productivity, recruiter time, and the cost of a bad hire.
the hidden talent problem
here's what nobody talks about: the best engineers often have terrible LinkedIn profiles. they're busy shipping code, not optimizing their personal brand.
Stack Overflow's 2024 Developer Survey shows that 84% of developers are employed and most aren't actively job hunting - they're heads-down building. but their GitHub? thousands of commits. their open source contributions? real, verifiable work.
LinkedIn's research on skills-first hiring backed by OECD data shows the business case is clear - yet most companies still rely on traditional resume screening that misses these people entirely.
traditional recruiting misses these people entirely because it's stuck looking at keywords on resumes.
the controversial questions
"won't AI just replace recruiters?" no. AI is a tool, not a replacement. the human judgment for culture fit, negotiation, candidate experience - that's irreplaceable. what AI does is eliminate the manual grunt work so recruiters can focus on what actually matters. HBR's latest research confirms - AI augments recruiters, but companies need to resist treating it as a cure-all.
"isn't this just another ATS?" ATS systems filter out candidates based on keywords. research consistently shows that automated systems reject the majority of qualified candidates who don't match exact keyword criteria. we do the opposite - we find talent that traditional systems would miss. we look at actual work: code, contributions, projects.
"what about bias?" fair question. resumes are inherently biased - name, school, previous companies. HBR's 2025 research on AI and fairness shows that AI often reshapes what fairness means rather than eliminating bias. when you evaluate actual code and contributions, you're looking at work output, not background. it's not perfect, but it's a step toward more objective evaluation.
what snapcore does
we aggregate signals from where engineers actually spend time: GitHub, Stack Overflow, personal projects, open source contributions. then we use AI to understand their actual skills - not just what they claim on a resume.
the result? recruiters get pre-qualified candidates with verified skills. engineers get discovered for their actual work. everyone saves time.
we are not building what's trending, we build what's missing.